Abstract: Respective energy consumption data is collected via respective agents running on respective host servers. The respective energy consumption data represents energy consumed by the respective host servers over a time period. The respective agents communicate with hardware on each of the respective host servers using a unified application programming interface (API). Respective energy costs are determined over the time period for the respective host servers based on the respective energy consumption data. A subset of the respective host servers that are being underutilized is identified based on the respective energy consumption data and the respective energy costs. An action to take with respect to the subset of the respective host servers that are being underutilized is determined to reduce the energy costs.

Abstract: Example implementations relate to proactively protecting service endpoints based on deep learning of user location and access patterns. A machine-learning model is trained to recognize anomalies in access patterns relating to endpoints of a cloud-based service by capturing metadata associated with user accesses. The metadata for a given access includes information regarding a particular user that initiated the given access, a particular device utilized, a particular location associated with the given access and specific workloads associated with the given access. An anomaly relating to an access by a user to a service endpoint is identified by monitoring the access patterns and applying the machine-learning model to metadata associated with the access. Based on a degree of risk to the cloud-based service associated with the identified anomaly, a mitigation action is determined. The cloud-based service is proactively protected by programmatically applying the determined mitigation action.

Abstract: Respective energy consumption data is collected via respective agents running on respective host servers. The respective energy consumption data represents energy consumed by the respective host servers over a time period. The respective agents communicate with hardware on each of the respective host servers using a unified application programming interface (API). Respective energy costs are determined over the time period for the respective host servers based on the respective energy consumption data. A subset of the respective host servers that are being underutilized is identified based on the respective energy consumption data and the respective energy costs. An action to take with respect to the subset of the respective host servers that are being underutilized is determined to reduce the energy costs.